def test_input_validation(self):
match = r"`order` must be either 1, 3, or 5."
with pytest.raises(ValueError, match=match):
NumericalInverseHermite(StandardNormal(), order=2)
match = "`cdf` required but not found"
with pytest.raises(ValueError, match=match):
NumericalInverseHermite("norm")
match = "could not convert string to float"
with pytest.raises(ValueError, match=match):
NumericalInverseHermite(StandardNormal(), u_resolution='ekki')
match = "`max_intervals' must be..."
with pytest.raises(ValueError, match=match):
NumericalInverseHermite(StandardNormal(), max_intervals=-1)
match = "`qmc_engine` must be an instance of..."
with pytest.raises(ValueError, match=match):
fni = NumericalInverseHermite(StandardNormal())
fni.qrvs(qmc_engine=0)
if NumpyVersion(np.__version__) >= '1.18.0':
# issues with QMCEngines and old NumPy
fni = NumericalInverseHermite(StandardNormal())
match = "`d` must be consistent with dimension of `qmc_engine`."
with pytest.raises(ValueError, match=match):
fni.qrvs(d=3, qmc_engine=stats.qmc.Halton(2))
def test_QRVS(self, qrng, size_in, size_out, d_in, d_out):
dist = StandardNormal()
fni = NumericalInverseHermite(dist)
# If d and qrng.d are inconsistent, an error is raised
if d_in is not None and qrng is not None and qrng.d != d_in:
match = "`d` must be consistent with dimension of `qmc_engine`."
with pytest.raises(ValueError, match=match):
fni.qrvs(size_in, d=d_in, qmc_engine=qrng)
return
# Sometimes d is really determined by qrng
if d_in is None and qrng is not None and qrng.d != 1:
d_out = (qrng.d, )
shape_expected = size_out + d_out
qrng2 = deepcopy(qrng)
qrvs = fni.qrvs(size=size_in, d=d_in, qmc_engine=qrng)
if size_in is not None:
assert (qrvs.shape == shape_expected)
if qrng2 is not None:
uniform = qrng2.random(np.prod(size_in) or 1)
qrvs2 = stats.norm.ppf(uniform).reshape(shape_expected)
assert_allclose(qrvs, qrvs2, atol=1e-12)
def test_QRVS_size_tuple(self):
# QMCEngine samples are always of shape (n, d). When `size` is a tuple,
# we set `n = prod(size)` in the call to qmc_engine.random, transform
# the sample, and reshape it to the final dimensions. When we reshape,
# we need to be careful, because the _columns_ of the sample returned
# by a QMCEngine are "independent"-ish, but the elements within the
# columns are not. We need to make sure that this doesn't get mixed up
# by reshaping: qrvs[..., i] should remain "independent"-ish of
# qrvs[..., i+1], but the elements within qrvs[..., i] should be
# transformed from the same low-discrepancy sequence.
if NumpyVersion(np.__version__) <= '1.18.0':
pytest.skip("QMC doesn't play well with old NumPy")
dist = StandardNormal()
fni = NumericalInverseHermite(dist)
size = (3, 4)
d = 5
qrng = stats.qmc.Halton(d, seed=0)
qrng2 = stats.qmc.Halton(d, seed=0)
uniform = qrng2.random(np.prod(size))
qrvs = fni.qrvs(size=size, d=d, qmc_engine=qrng)
qrvs2 = stats.norm.ppf(uniform)
for i in range(d):
sample = qrvs[..., i]
sample2 = qrvs2[:, i].reshape(size)
assert_allclose(sample, sample2, atol=1e-12)